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7,958 results on '"A. Bejan"'

1. Locomotion rhythm makes power and speed

Author

A. Bejan, U. Gunes, and H. Almahmoud

Subjects
Medicine, Science
Abstract

Abstract This article addresses two questions, why certain animals (frogs, breaststroke swimmers, hovering fliers, jellyfish) push rapidly against the surrounding fluid and then reach forward slowly, and whether this rhythm of propulsion is a manifestation of the universal phenomenon of design evolution in nature. Emphasis is on the distribution of time periods of locomotion in which, during the driving phase of cyclic movement (the motive stroke, phases 1 and 2, in alternating sequence with the dissipative stroke, phase 3), the work is generated (phase 1) and dissipated (phase 2). The relative lengths of the characteristic times t1 and t2 of the phases 1 and 2, are predicted. The relative duration of the proposed three phases of a cycle is the ‘rhythm’. The analysis is based on a model of how the effective cross-sections of the stroking body parts impact the surrounding medium, water, or air, and the total power required to account for the kinetic energy losses during phases 2 and 3, which are due to drag forces posed by the surrounding medium. The body configuration (limbs' cross-sections) determines the limbs' velocities that maximize mean power, and the times t1 and t2 within the motive stroke. Emphasis is placed on the freedom to change the evolving design. Freedom is represented in two ways: the number of degrees of freedom in changing the dimensions of the model and its deformation in time, and the effect that evolutionary changes have on the access that the body has to its available space. Freedom to change the locomotion design leads to greater power and speed.

Published
2023
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2. Matchgate circuits deeply thermalize

Author

Bejan, Mircea, Béri, Benjamin, and McGinley, Max

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Statistical Mechanics, Mathematical Physics
Abstract

We study the ensemble of states generated by performing projective measurements on the output of a random matchgate (or free-fermionic) quantum circuit. We rigorously show that this `projected ensemble' exhibits deep thermalization: For large system sizes, it converges towards a universal ensemble that is uniform over the manifold of Gaussian fermionic states. As well as proving moment-wise convergence of these ensembles, we demonstrate that the full distribution of any physical observable in the projected ensemble is close to its universal form in Wasserstein-1 distance, which we argue is an appropriate and efficiently computable measure of convergence when studying deep thermalization. Using this metric, we also numerically find that local matchgate circuits deeply thermalize after a timescale $t \sim L^2$ set by the diffusive spreading of quantum information. Our work opens up new avenues to experimentally accessible protocols to probe the emergence of quantum statistical mechanics and benchmark quantum simulators., Comment: 6+17 pages, 8 figures

Published
2024

5. Polygenic risk score for ulcerative colitis predicts immune checkpoint inhibitor-mediated colitis.

Author

Thummalapalli, Rohit, Betti, Michael, Yao, Lydia, Balaratnam, Karmugi, Bejan, Cosmin, Cardenas, Eduardo, Falcon, Christina, Faleck, David, Gubens, Matthew, Huntsman, Scott, Johnson, Douglas, Kachuri, Linda, Khan, Khaleeq, Li, Min, Lovly, Christine, Murray, Megan, Patel, Devalben, Werking, Kristin, Xu, Yaomin, Zhan, Luna, Balko, Justin, Liu, Geoffrey, Aldrich, Melinda, Schoenfeld, Adam, Ziv, Elad, Quandt, Zoe, and Middha, Pooja

Subjects
Humans, Colitis, Ulcerative, Immune Checkpoint Inhibitors, Carcinoma, Non-Small-Cell Lung, Genetic Risk Score, Lung Neoplasms, Colitis, Crohn Disease
Abstract

Immune checkpoint inhibitor-mediated colitis (IMC) is a common adverse event of treatment with immune checkpoint inhibitors (ICI). We hypothesize that genetic susceptibility to Crohns disease (CD) and ulcerative colitis (UC) predisposes to IMC. In this study, we first develop a polygenic risk scores for CD (PRSCD) and UC (PRSUC) in cancer-free individuals and then test these PRSs on IMC in a cohort of 1316 patients with ICI-treated non-small cell lung cancer and perform a replication in 873 ICI-treated pan-cancer patients. In a meta-analysis, the PRSUC predicts all-grade IMC (ORmeta=1.35 per standard deviation [SD], 95% CI = 1.12-1.64, P = 2×10-03) and severe IMC (ORmeta=1.49 per SD, 95% CI = 1.18-1.88, P = 9×10-04). PRSCD is not associated with IMC. Furthermore, PRSUC predicts severe IMC among patients treated with combination ICIs (ORmeta=2.20 per SD, 95% CI = 1.07-4.53, P = 0.03). Overall, PRSUC can identify patients receiving ICI at risk of developing IMC and may be useful to monitor patients and improve patient outcomes.

Published
2024

8. Polygenic risk score for ulcerative colitis predicts immune checkpoint inhibitor-mediated colitis

Author

Middha, Pooja, Thummalapalli, Rohit, Betti, Michael J., Yao, Lydia, Quandt, Zoe, Balaratnam, Karmugi, Bejan, Cosmin A., Cardenas, Eduardo, Falcon, Christina J., Faleck, David M., Gubens, Matthew A., Huntsman, Scott, Johnson, Douglas B., Kachuri, Linda, Khan, Khaleeq, Li, Min, Lovly, Christine M., Murray, Megan H., Patel, Devalben, Werking, Kristin, Xu, Yaomin, Zhan, Luna Jia, Balko, Justin M., Liu, Geoffrey, Aldrich, Melinda C., Schoenfeld, Adam J., and Ziv, Elad

Published
2024
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10. Dynamical Magic Transitions in Monitored Clifford+T Circuits

Author

Bejan, Mircea, McLauchlan, Campbell, and Béri, Benjamin

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Statistical Mechanics
Abstract

The classical simulation of highly-entangling quantum dynamics is conjectured to be generically hard. Thus, recently discovered measurement-induced transitions between highly entangling and low-entanglement dynamics are phase transitions in classical simulability. Here, we study simulability transitions beyond entanglement: noting that some highly-entangling dynamics (e.g., integrable systems or Clifford circuits) are easy to classically simulate, thus requiring "magic"--a subtle form of quantum resource--to achieve computational hardness, we ask how the dynamics of magic competes with measurements. We study the resulting "dynamical magic transitions" focusing on random monitored Clifford circuits doped by T gates (injecting magic). We identify dynamical "stabilizer-purification"--the collapse of a superposition of stabilizer states by measurements--as the mechanism driving this transition. We find cases where transitions in magic and entanglement coincide, but also others with a magic and simulability transition in a highly (volume-law) entangled phase. In establishing our results, we use Pauli-based computation, a scheme distilling the quantum essence of the dynamics to a magic state register subject to mutually commuting measurements. We link stabilizer-purification to "magic fragmentation" wherein these measurements separate into disjoint, O(1)-weight blocks, and relate this to the spread of magic in the original circuit becoming arrested., Comment: 28 pages, 12 figures; v3: accepted manuscript

Published
2023
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11. Finite element 3-D model of a double quantum ring: effects of electric and laser fields on the interband transition

Author

Radu, Adrian, Stan, Cristina, and Bejan, Doina

Subjects
Physics - Computational Physics, J.2
Abstract

In this work, the changes in the energy of electrons and holes, oscillator strength and interband transition time when external fields are applied to a GaAs/AlGaAs semiconductor double ring grown by the droplet epitaxy technique are theoretically analyzed. We consider a static electric field and an intense laser field nonresonant with the quantum structure, with variable intensities and orientations with respect to the symmetry axis of the quantum ring. In the formalism of the effective mass approximation for electrons and holes, the energies and wavefunctions were numerically computed using the finite element method implemented with an accurate three-dimensional model of the real quantum ring. Laser dressing of the confining potential was performed using the exact integration formula at each point. Our results show major differences between the effects of the two types of applied fields, caused mainly by the static electric-field-induced strong polarizability of the confined electron-hole pair. In addition, the effects of both fields exhibit strong anisotropy in the electronic properties as a result of the particular flattened geometry of the quantum ring. Proper combinations of field strengths and orientations are helpful in designing accurate tools for the sensitive manipulation of interband radiative properties., Comment: 23 pages, 15 figures, 3 tables

Published
2023

14. The use of robot-assisted surgery for the unstable traumatic spine: A retrospective cohort study.

Author

Diaz-Aguilar, Luis, Bui, Nicholas, Alvandi, Bejan, Pennington, Zach, Gendreau, Julian, Jeswani, Sunil, Nguyen, Andrew, Pham, Martin, Santiago-Dieppa, David, and Brown, Nolan

Subjects
Imaging, Radiograph, Registration, Robotics, Spine, Trauma
Abstract

BACKGROUND: Robotic assistance has been shown to increase instrumentation placement accuracy in open and minimally invasive spinal fusion. These gains have been achieved without increases in operative times, blood loss, or hospitalization duration. However, most work has been done in the degenerative population and little is known of the utility of robotic assistance when applied to spinal trauma. This is largely due to the uncertainty stemming from the disruption of normal anatomy by the traumatic injury. Since the robot depends upon registration for instrumentation guidance according to the fiducials it uses, trauma can introduce unique challenges. The present study sought to evaluate the safety and efficacy of robotic assistance in a consecutive cohort of spine trauma patients. METHODS: All patients with Thoracolumbar Injury Classification and Severity Scale (TLICS) >4 who underwent robot-assisted spinal fusion using the Globus ExcelsiusGPS at a single tertiary care center for trauma between 2020 and 2022 were identified. Demographic, clinical, and surgical data were collected and analyzed; the primary endpoints were operative time, fluoroscopy time, estimated blood loss, postoperative complications, admission time, and 90-day readmission rate. The paired t-test was used to compare differences between mean values when looking at the number of surgical levels. RESULTS: Forty-two patients undergoing robot-assisted spinal surgery were included (mean age 61.3±17.1 year; 47% female. Patients were stratified by the number of operative levels, 2 (n = 10), 3-4 (n = 11), 5 to 6 (n = 13), or >6 (n = 8). There appeared to be a positive correlation between number of levels instrumented and odds of postoperative complications, admission duration, fluoroscopy time, and estimated blood loss. There were no instances of screw malposition or breach. CONCLUSIONS: This initial experience suggests robotic assistance can be safely employed in the spine trauma population. Additional experiences in larger patient populations are necessary to delineate those traumatic pathologies most amenable to robotic assistance.

Published
2023

15. Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets

Author

Bejan, Irina, Sokolov, Artem, and Filippova, Katja

Subjects
Computer Science - Computation and Language
Abstract

Increasingly larger datasets have become a standard ingredient to advancing the state-of-the-art in NLP. However, data quality might have already become the bottleneck to unlock further gains. Given the diversity and the sizes of modern datasets, standard data filtering is not straight-forward to apply, because of the multifacetedness of the harmful data and elusiveness of filtering rules that would generalize across multiple tasks. We study the fitness of task-agnostic self-influence scores of training examples for data cleaning, analyze their efficacy in capturing naturally occurring outliers, and investigate to what extent self-influence based data cleaning can improve downstream performance in machine translation, question answering and text classification, building up on recent approaches to self-influence calculation and automated curriculum learning., Comment: Published at EMNLP 2023

Published
2023

17. Generalized Priority Models for QoS and CoS Network Technologies

Author

Gh. Mishkoy, S. Giordano, A. Bejan, and O. Benderschi

Subjects
Priority queues, switchover times, Quality of Service (QoS), Class of Service (CoS), prioritization, traffic characteristics, Electronic computers. Computer science, QA75.5-76.95
Abstract

The variety of priority queueing systems with random switchover times is suggested in this paper. Such systems represent generalized models for a wide class of phenomena which involve queueing and prioritization and are considered in QoS and CoS network problems. The classification of such systems is given and methods of their analysis are discussed. Specialists in QoS and CoS technologies may find such models adequate and appropriate for the network traffic analysis. 1991 Mathematics Subject Classification: Primary 90B22; Secondary 68M22, 68M10.

Published
2007

18. Constructal theory of pattern formation

Author

A. Bejan

Subjects
Technology, Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350
Abstract

This review article shows that the occurrence of macroscopic flow configuration is a universal natural phenomenon that can be explained and predicted on the basis of a principle of physics (the constructal law): "For a flow system to persist in time (to survive) it must evolve in such a way that it provides easier and easier access to the currents that flow through it". The examples given in this article come from natural inanimate flow systems with configuration: duct cross-sections, open channel cross-sections, tree-shaped flow architectures, and turbulent flow structure (e.g., eddies, laminar lengths before transition). Other examples that are treated in the literature, and which support the constructal law, are the wedge-shape of turbulent shear layers, jets and plumes, the frequency of vortex shedding, Bénard convection in fluids and fluid-saturated porous media, dendritic solidification, the coalescence of solid parcels suspended in a flow, global atmospheric and oceanic circulation and climate, and virtually all architectural features of animal design. The constructal law stresses the importance of reserving a place for pure theory in research, and for constantly searching for new physics – new summarizing principles that are general, hence useful.

Published
2007

22. Lightweight Concrete with Wastes Used for Hollow Concrete Blocks

Author

Hanuseac, L. G., Bejan, G., Mihai, P., Bărbuță, M., Panțiru, A., Mihai, R. G., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Tuns, Ioan, editor, Muntean, Radu, editor, Radu, Dorin, editor, Cazacu, Christiana, editor, and Gălățanu, Teofil, editor

Published
2024
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23. Mechanical Properties of Lightweight Concrete with Saw Dust Waste

Author

Morariu, Adrian, Pantiru, Alexandru, Bejan, Gabriel, Barbuta, Marinela, Elisei, Sorin, Rosu, Raluca, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Moldovan, Liviu, editor, and Gligor, Adrian, editor

Published
2024
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26. Tranexamic acid versus placebo to prevent bleeding in patients with haematological malignancies and severe thrombocytopenia (TREATT): a randomised, double-blind, parallel, phase 3 superiority trial

Author

Estcourt, Lise J, McQuilten, Zoe K, Bardy, Peter, Cole-Sinclair, Merrole, Collins, Graham P., Crispin, Philip J., Curnow, Elinor, Curnow, Jennifer, Degelia, Amber, Dyer, Claire, Friebe, Adam, Floro, Lajos, Grand, Effie, Hudson, Cara, Jones, Gail, Joseph, Joanne, Kallmeyer, Charlotte, Karakantza, Marina, Kerr, Paul, Last, Sara, Lobo-Clarke, Maria, Lumley, Matthew, McMullin, Mary F, Medd, Patrick G., Morton, Suzy M., Mumford, Andrew D., Mushkbar, Maria, Parsons, Joseph, Powter, Gillian, Sekhar, Mallika, Smith, Laura, Soutar, Richard, Stevenson, William S., Subramoniapillai, Elango, Szer, Jeff, Thomas, Helen, Waters, Neil A., Wei, Andrew H., Westerman, David A., Wexler, Sarah A., Wood, Erica M., Stanworth, Simon J., Abioye, Adrienne, Afghan, Rabia, Ai, Sylvia Ai, Akanni, Magbor, Alajangi, Rajesh, Alam, Usmaan, Al-Bubseeree, Bahaa, Alderson, Sophie, Alderson, Craig, Ali, Sayed, Ali, Kabir, Alighan, Rookmeen, Allam, Rebecca Allam, Allen, Tania, Al-Sakkaf, Wesam, Ames, Kate, Anderson, Jacqueline, Andrews, Colin, Angel, Ann-Marie, Anlya, Manuela Anlya, Ansari, Farah, Appleby, Rowan, Arnold, Claire, Asbjornsdottir, Hulda, Asfaw, Biruk, Atkins, Elissa, Atkinson, Leela, Aubrey, Clare, Ayesha, Noor, Babbola, Lola, Badcock, David, Badcock, Samuel, Baggio, Diva, Bailiff, Ben, Baines, Kizzy, Baker, Holly, Baker, Victoria, Ball, Lindsay, Ball, Martin, Balquin, Irwin, Banks, Emma, Banos, George, Barnett, Jaytee, Barrie, Claire, Barron, Claire, Barton, Rebecca, Bason, Nina, Batta, Bindu, Bautista, Dianne, Bayley, Angela, Bayly, Emma, Baynes, Fionnuala, Bazargan, Ali, Bazeley, Rachel, Beadle, Yvonne, Beardsmore, Claire, Beattie, Kate, Bedford, Caroline, Behal, Rachna, Behan, Daniel, Bejan, Lilihna, Bell, Sarah, Bell, Karen, Bell, Louise, Bell, Kaitlyn, Benjamin, Reuben, Bennett, Sam, Benson, Gary, Benson, Warwick, Bent, Cameron, Bergin, Krystal, Berry, Alex, Besenyei, Stephanie, Besley, Caroline, Betteridge, Scott, Beveridge, Leigh, Bhattacharyya, Abir, Billen, Annelies, Bilmon, Ian, Binns, Emma, Birt, Mark, Bishop, David, Blanco, Andrea, Bleby, Lisa, Blemnerhet, Richard, Blombery, Piers, Blyth, Emily, Blythe, Nicola, Boal, Lauren, Boden, Ali, Bokhari, Syed W.I., Bongetti, Elisa, Booth, Stephen, Borley, Jayne, Bowen, David, Bowers, Dawn, Boyd, Stephen, Bradley, Sarah, Bradman, Helen, Bretag, Peta, Brillante, Maria, Brockbank, Rachel, Brough, Yasmin, Brown, Ellen, Brown, Jo, Brown, Eleanor, Brown, Claire, Brown, Jenny, Brown, Susan, Browning, Joe, Brownsdon, Alex, Bruce, David, Brydon-Hill, Ruth, Buckwell, Andrea, Burgess, Dannielle, Burke, Glenda, Burley, Kate, Burney, Claire, Burns, David, Burrows, Samuel, burton, Kieran, Butler, Jason, Cambalova, Lenka, Camozzi, Maria C., Campbell, Philip, Campfield, Karen, Campion, Victoria, Cargo, Catherine, Carmona, Julia, Carney, Dennis, Casan, Joshua, Cashman, Helen, Catt, Lorraine, Cattell, Michael, Cavill, Megan, Chadbone, Rachel, Chaganti, Sridhar, Chai, Yee, Chai, Khai Li, Chang, Joshua, Chapman, Judith, Chapman, Oliver G., Chapter, Tamika, Charlton, Andrew, Chau, Celina, Chauhan, Saleena, Chavda, Nikesh, Chen, Frederick, Chen, Melody, Chen, Meng Xi, Chen, Melanie, Chen, Melissa, Cheok, Kathleen, Cheung, Mai, Chidgey, Luke, Chmielokliec, Karolina, Choi, Philip, Choi, Jae, Chok, Anne, Chopra, Ruchika, Christopherson, Louise, Chu, Vicky, Chua, Chong Chyn, Chudakou, Pavel, Chugh, Vidushi, Chung, Chi, Clark, Erin, Clarke, Peter, Clarke, Kathleen, Clay, Jennifer, Clayton, Laura, Clements, Mitch, Clemmens, Jonathan, Clifford, Ruth, Collett, Dave, Collins, Maia, Collyer, Emily, Connolly, Maureen, Cook, Mark, Coombs, Sarah, Coppell, Jason, Cornwell, Sophie, Corrigan, Claire, Coughlin, Elizabeth, Couling, Jennifer, Cousins, Tony, Cowan, Catriona, Cox, Christine, Cox, Catherine, Coyle, Luke, Craig, Emily, Creasey, Thomas, Croan, Laura, Croft, Jane, Crosbie, Nicola, Crowe, Josephine, Crowther, Helen, Crozier, Jane, Culleton, Naomi, Cullis, Jonathan, Cumming, Anita, Cummins, Michelle, Cunningham, Adam, Curley, Cameron, Curtis, Samantha, Cuthbert, Robert, Cuthill, Kirsty, Dahahayake, Dinusha A, Dang, Amy, Davies, Marc, Davies, Ceri, Dawson, Emily, Day, Tom, De Abrew, Kanchana, De Lavallade, Hugues, De Silva, Neelaskshi, Dean, Georgina, Deane, Christopher, Demosthenous, Lisa, Desai, Amisha, Desborough, Michael, Devanny, Ian, Dhanapal, Jay, Dhani, Sundip, Di Martino, Vicky, Dickens, Emmy, DiCorleto, Carmen, Dinnett, Louise, Dirisan, Divya, Dixon, Karen, Dixon, Kiri, Doal, Inderjit, Dobivh, J, Docanto, Maria, Doecke, Helve, Donaldson, David, Donaldson, Kylee, Donohoe, Carrie, Douglas, Ashley, Doung, Stephen, Downer, Susan, D'Rozario, James, Drummond, Malcolm, Drummond, Mark, Drummond, Samantha, Drysdale, Elizabeth, D'Souza, Ross, D'Souza, Eugene, Dunn, Alex, Dutton, David, Dyson, Martin, Ediriwicurena, Kushani, Edleston, Sharon, Edwards, Dawn, Edwards, Morgan, Edwards, Anita, Eise, Nicole, Ellis, Steven, Ellis, Hayley, Elmonley, Shareef, Enstone, Rosemarie, Eordogh, Agnes, Erb, Sharon, Evans, Shannon, Evans, Megan, Ewing, Joanne, Eyre, Toby, Facey, Adam, Fammy, Mina, Farman, Jon, Farnell, Rachel, Favero, Laura, Fay, Keith, Ferguson, Karen, Fernon, Laura, Filshie, Robin, Finnegan, Damian, Fisher, Lisa, Flanagan, Asia, Fleck, Emma, Fletcher, Simon, Flora, Harpreet, Flower, Catherine, Fodor, Ioana, Foley, Heather, Folland, Emma, Folorunso, Comfort, Forbes, Molly, Fordwor, Katrina, Foster, Polly, Fox, Vanessa, Fox, Thomas, Francis, Olesya, Fryearson, Louise, Fuery, Madonna, Fung, Jiin, Furtado, Michelle, Galloway-Browne, Leanne, Gamble, Louise, Gamgee, Jeanette, Ganapathy, Arundathi, Gardner, Hayley, Gardner, Clare, Gasmelsheed, Noha, Gately, Amy, Gaynor, Lynda, Gebreid, Alex, Geffens, Ruth, George, Rachel, Gertner, Aniko, Ghebeh, Manar, Ghirardini, Emanuela, Giddings, Melainie, Gillett, Sandra, Gillett, Karen, Giri, Pratyush, Glass, Chris, Glewis, Sarah, Gooding, Sarah, Gordon, Olivia, Gordon, Joanne, Gottlieb, David, Gowda, Koushik, Gower, Elysie, Gray, Nicola, Grayer, Jo, Greaves, Elaine, Greenaway, Sally Anne, Greenfield, Graeme, Greenwood, Matthew, Gregory, Gareth, Griffin, James, Griffith, Julia, Griffith, James, Griffiths, Lindsey, Grzegrzolka, Paulina, Gu, Yisu, Guest, Jo, Guinai, Rosanna, Gullapalli, Veena, Gunolr, A., Guo, Lina, H, Wayne, Hagua, Sophia, Haile, Senait, Hall, Richard, Hamdollah-Zadeh, Maryam, Hanif, Zahra, Hanlon, Kathleen, Hann, Nicholas, Hanna, Ramez, Hannah, Guy, Hapuarachchi, Sameera, Hardman, Jacinta, Hardy, Alison, Harris, Anthony, Harris, Kylie, Harrison, Beth, Harrison, Simon, Harrison, Lea-Anne, Harrop, Sean, Harvey, Caroline, Hatcliffe, Faye, Hawking, Jo, Hawkins, Matthew, Hayden, Janet, Hayman, Michelle, Haynes, Elizabeth, Heaney, Nicholas, Hebbard, Andrew, Hempton, Jenny, Hendunneti, Sasanka, Henry, Maeve, Heywood, Jonathan, Hildyard, Catherine, Hill, Lydia, Hilldrith, Annette, Hitev, Petar, Hiwase, Smita, Hiwase, Devendra, Hoare, Chris, Hodge, Renate, Holloway, Amy, Holt, Chloe, Holton, Kelly, Homer, Lauren, Horne, Gillian, Horvath, Noemi, Hotong, Linda, Houdyk, Kristen, Houseman, Katy, Hoxhallari, Ilda, Hsu, Hannah, Hsu, Nina, Huang, Gillian, Hudson, Kerryn, Hufton, Melanie, Hughes, Timothy, Hughes, Siobhan, Hurley, Kate, Huxley, Rosie, Ibitoye, Temitope, Ibrouf, Abubaken, Inam, Farha, Indran, Tishya, Ingham, Karen, Innes, Calum, Irvine, David, Jaafar, Sarah, Jain, Manish, Jameson, Laura, Janjua, Pardeep, Jarvis, Rebecca, Jatheendran, Abirami, Javed, Abbie, Jen, Sheila, Jobanpura, Shailesh, Jobson, Irene, John, Deborah, Johns, Sophie, Johnston, Amanda, Jones, Hollie, Jones, Francesca, Joniak, Karolina, Jovanovic, Michael, Jovic, Anita, Joyce, Lauren, Judd, Andrew, Kakarlamudi, Sudhakar, Kakaroubas, Nick, Kalita, Maggie, Kam, Shirly, Kan, Julie, Kandle, P, Kanellopoulos, Alex, Kao, Chien, Kaparou, Maria, Kartsios, Charamlampos, Katsioulas, Vicki, Kaye, Russell, Keen, Katie, Kelly, Richard, Kelly, Pauline, Kelly, Donna, Kelly, Melanie, Kennedy, Glen, Kennedy, Nola, Kenny, Angela, Kenworthy, Zoe, Kerridge, Ian, Kesavan, Murali, Khafizi, Angelika, Khakwani, Muhammad, Khalid, Amna, Khamly, Kate, Khan, Anjum, Khan, Dalia, Khan, Mojid, Khan, Lubna, Khoo, Mona, Khwaja, Asim, Kim, Grace, King, Andrew, King, Vicky, King, Donna, Kinsella, Francesca, Kipp, David, Kirandeep, Pachoo, Kirui, Laura C., Kishore, Bhuvan, Knectlhi, Christopher, Knot, Amy, Knot, Armit, Ko, Cathy, Kolaric, Caitlin, Koo, Ray, Kotadia, Mary, Kothari, Jaimal, Kottaridis, Panagiotis D., Kuiluinathan, Gajan, Kulasekararaj, Austin, Kwan, John, Kwok, Marwan, Kwok, Phillip, Kwok, Fiona, Laane, Kristiina, Lad, Deena, Laird, Jennifer, Lam, Ada, Lane, Mary, Lanenco, Monica, Lang, Susan, Langridge, Alex, Langton, Catherine, Lannon, Michelle, Latif, Annie, Latimer, Maya, Latter, Ruth, Lau, I-Jun, Lawless, Sarah, Lawless, Theresa, Leach, Mike, Leaney, Sarah, Leary, Heather, Leavy, James, LeBlanc, Abbey, Lee, Vivienne, Lee, Edwin, Lee, Jenny, Lee, Tamara, Leischkie, Marian, Leitinger, Emma, Leon, Christopher, Leonard, Jayne, Lewis, David, Lewis, Ian, Lewis, Tania, Lim, Daniel, Littlewood, Kelly, Liu, Dara, Loh, Joanna, Lokare, Anand, Lomas, Oliver, Lovell, Richard, Lowe, Theresa, Lowry, Lisa, Lubowiecki, Marcin, Lumb, Rebecca, Lynch, Gail, Macaulay, Amanda, MacDonald, Lyndsey, MacDonald-Burn, Jill, Macmillan, Margaret, Maddock, Karen, Mahaliyana, Tomas, Mahon, Cassandra, Maidment, Alison, Maier, Susie, Mairos, Michelle, Majid, Mahseeman, Mak, Ka L, Mak, Anne, Malendrayogau, Arunthrthy, Malham, Hana, Malyon, Felicity, Mandadapu, Vineela, Mandel, Laura, Mant, Sarah, Manton, Ruth, Maouche, Nadjoua, Maqbool, Muhammad G., Marchant, Gregory, Marinho, Mariana, Marks, David, Marner, Mike, Marr, Helen, Marshall, Gillian, Martin, Siobhan, Martin, Abigail, Marzolini, Maria, Mason, Kiara, Massie, Jonathan, Masson, Rebecca, Mathavan, Vidya, Mathew, Siju, Mathie, Judith, Mattocks, Lehenta, Maybury, Bernard, Mayer, Georgina, McAlister, Chyrelle, McAllister, Jo, McConnell, Stewart, McCracken, James, McCullagh, Liz, McCulloch, Rory, Mcdermott, Christopher, Mcdonald, Kerian, McGinniss, Laura, McGurk, Fiona, McIlwain, Jessica, McIver, Kirsten, Mckay, Pam, McKenna, Lorraine, Mclornan, Donal, McMahon, Coalon, McNeice, Linda, McNeill, Susan, McNickle, Molly, McQueen, Fiona, McRae, Simon, McTaggart, Bobby, Mehew, Jenny, Mehra, Varnn, Melly, Michelle, Menichelli, Tara, Micklethwatte, Ken, Mihailescue, Loredana, Mijovic, Aleksander, Millband, Hannah, Miller, Lucy, Millien, Samuel T., Milnthorpe, James, Minson, Adrian, Molnar, Eva, Monsour, Marc, Moody, Mary, Moon, Rebecca, Moore, Sally, Moore, Katy, Morgan, Kelly, Morralley, Rebecca, Morris, Denise, Morris, Kirk, Morrison, Nicole, Moss, Merinda, Mughal, Muhammad, Muir, Paul, Mukkath, David, Mulla, Aasiyu, Mulligan, Stephen, Mullings, Joanne, Mulqueen, Angela, Muluey, Caitlin, Murdoch, Sarah, Murrani, Sura, Murthy, Vidhya, Musngi, Jimmy, Mustafa, Nadreen, Mynes, Tracey, Nalpantidis, Anastasios, Nandurkar, Harshal, Nardone, Linda, Nasari, Latifa, Nash, Monica, Naylor, Georgina, Ngu, Loretta, Nguethina, Melissa, Nguyen, John, Nguyen, Joseph, Nichol, Wendy, Nicholls, Emma, Nicole, Catherine S., Nicolson, Phillip, Nielson, David, Nikolousis, Emmanouil, Nix, Georgina, Njoku, Rita, Norman, Jane, Norman, Amy, Norris, Phoebe, North, Daniel, Norwood, Megan, Notcheva, Gaynor, Novitzky-Basso, Igor, Nyaboko, Joseph, Nygren, Maria, Obu, Ingrid, O'Connell, Siobhan, O'Connor, Jody, O'Kelly, Deanna, O'Niell, Aideen, Ony, Jeremy, Oo, Kathy, Oo, April, Oppermann, Anne, Orr, Ruth, O'Sullivan, Mary, Page, Jennifer, Palfreyman, Emma, Paneesha, Shankaranarayana, Panicker, Shyam, Parbutt, Catherine, Parigi, Elesha, Paris, Gemma, Parker, Tracey, Parnell, Caroline, Parrish, Christopher, Parsons, Alex, Pasat, Mioara, Patel, Natasha, Patel, Vijay, Patel, Pooja, Patel, Chaya, Pati, Nalini, Patterson, Andrea, Paul, Lauren, Payet, Danielle, Payne, Elspeth, Peachey, Victoria, Pearson, Amanda, Peniket, Andy, Percy, Laura, Pereyra, Millicent, Pervaiz, Omer, Phalod, Gunjan D, Pham, Anh, Pho, Jason, Pickard, Keir, Pidcock, Michael, Piggin, Anna, Pishyar, Yalda, Pocock, Abigail, Pol, Ranjendres, Polzella, Paolo, Poolan, Sonia, Portingale, Vicki, Posnett, Claire, Potluri, Sandeep, Potter, Victoria, Pratt, Guy, Prodger, Catherine, Pueblo, Andres, Puliyayil, Anish, Puvanakumar, Pratheepan, Qadri, Abdul, Quach, Hang, Quinn, Michael, Rafferty, Mark, Rahman, Marzia, Raj, Kavita, Raj, Sonia, Rajendran, Ramina, Ramanan, Radha, Ramasamy, Karthik, Rampotas, Alexandros, Ranchhod, Natasha, Rashid, Sabia, Ratanjee, Sunita, Rathore, Gurpreet, Ratnasingam, Sumita, Rayat, Manjit, Rayner, Michael, Reddell-Denton, Rebecca, Redding, Nicola, Reddy, Udaya, Rehman, Atique, Rice, Carol, Riches, Iwona, Rider, Thomas, Riley, John, Rinaldi, Ciro, Roberts, Kayleigh, Roberts, Andrew, Robertson, Bryony, Robertson, Peter, Robinson, Dan, Robinson, Rebecca, Robjohns, Emma, Robledo, Laura, Rodrigues, Ana, Rofe, Chris, Roff, Bridie, Rogers, Rachel, Rolt, Jill, Rooney, Carmela, Rose, Kathy, Rose, Hannah, Ross, David, Rouf, Shahara, Rourke, Claire, Routledge, David, Ruggiero, Janet, Rule, Simon, Rumsey, Richard, Sagge, Cherry, Saldhana, Helen, Salisbury, Richard, Salisbury, Sarah, Salvaris, Ross, Sanders, Kay, Sangombe, Mirriam, Sanigorska, Anna, Santos, Kristine, Sarkis, Taylah, Sarma, Anita, Saunders, Natalie, Schmidt, Kara, Schmidtmann, Anja, Schumacher, Ann, Scorer, Tom Scorer, Scott, Asleigh, Seath, Ingrid, Sejman, Frances, Selim, Adrian, Shamim, Nadia, Shan, Jocelyn, Shanmuganathan, Naranie, Shanmugaranjan, Shaminie, Sharpe, Michelle, Sharpley, Faye, Shaw, Emma, Sheath, Cara, Sheehy, Oonagh, Shen, Vivian, Sherbide, Solomon, Sheridan, Mathew, Sheridan, Jane, Sheridon, Matthew, Shields, Tracy, Sim, Hau V, Sim, Shirlene, Sims, Matt, Singaraveloo, Lydia, Singh, Gurcharan, Singh, Jasmine, Sladesal, Shree, Sloan, Andrew, Slobodian, Peter, Smith, Sophie, Smith, Sarit, Smith, Claire, Smith, Alastair, Smith, Neil, Snowden, Katherine, Solis, Joel, Somios, Denise, Soo, Jade, Spanevello, Michelle, Spaulding, Madeleie, Spence, Laura, Spillane, Liz, Spiteri, Alisha, Sprigg, Naomi, Springett, Sally, Stafford, Lynn, Stainthorp, Katherine, Stark, Kate, Steeden, Louise, Stephen, Ella, Stephenson, Aisling, Stewart, Andrew, Stewart, Orla, Stobie, Emma, Stokes, Chelsea, Streater, Jacqui, Suddens, Charlie-Marie, Suntharalingam, Surenthini, Surana, Narinder, Sutherland, Robyn, Sutherland, Antony, Sutton, David, Sweeney, Connor, Sweet, Reilly, Szucs, Aniko P, Taheri, Leila E., Tailor, Hinesh, Tam, Constantine, Tambakis, George, Tamplin, Mary, Tan, Chee, Tan, Sui, Tan, Joanne, Tan, Zhi, Taran, Tatiana, Tarpey, Fiona, Taseka, Angela, Tasker, Suzy, Tatarczuch, Maciej, Tayabali, Sarrah, Taylor, Hannah, Taylor, Robert, Taylor, Melaine, Taylor-Moore, Ella, Teasdale, Lesley, Tebbet, Elizabeth, Tedjasepstra, Aditya, Tedjaseputra, Aditya, Tepkumkun, Oummy, Terpstra, Andrew, Thomas, Wayne, Thomas, Shanice, Thompson, Rachel, Thornton, Thomas, Thorp, Bronwyn, Thrift, Moi Yap, Thwaites, Phillipa, Timbres, Jasmine, Tindall, Lauren, Tiong, Ing Soo, Tippler, Nicole, Todd, Tony, Todd, Shirley, Toghill, Neil, Tomlinson, Eve, Tooth, Jacinta, Topp, M., Trail, Nicola, Tran, Nguyen, Tran, Elizabeth, Tran, Vi, Treder, Bona, Tribbeck, Michelle, Trochowski, Siobhan, Truslove, Maria, Tse, Tsun, Tseu, Bing, Tucker, David, Turner, Kelly, Turner, Dianne, Turner, Herleen, Turner, Gillian, Twohig, Julie, Tylee, Thomas, Uhe, Micheleine, Underhill, Lauren, V, Joanne, Van der Vliet, Georgina, Van Tonder, Tina, VanderWeyden, Carrie, Varghese, Jerry, Vaughan, Lachlan, Veale, David, Vickaryyous, Nicky, Vince, Kathryn, Von Welligh, Jacoba, Vora, Sona, Wadehra, Karan, Walker, Rebecca, Walker, Stephen, Wallace, Roslyn, Wallniosve, Stephanie, Wallwork, S., Walmsley, Zoe, Walters, Fiona, Wang, Joyce, Wang, Angela, Wang, Chen, Wanyika, Mercy, Warcel, Dana, Wardrobe, Katrina, Warnes, Kristian, Waterhouse, Christopher, Waterworth, Adam, Watson, Caroline, Watson, Edmund, Watts, Emily, Weaver, Emma, Weber, Nicholas, Webley, Kaytie, Welford, Anna, Wells, Matt, Westbury, Sarah, Westcott, Jackie, Western, Robyn, Weston, Julia, White, Jessica, White, Phillipa, Whitehead, Anna, Whitehouse, James, Wieringa, Samantha, Willan, John, Williams, Sandra, Williams, Bethany, Williamson, Stephanie, Willoughby, Brett, Wilmot, Gail, Wilmott, Rosalind, Wilson, Joanna, Wilson, Emma, Wilson, Suzy, Wilson, Heather, Wilson, Caroline, Wilson, Tanya, Wilton, Margaret, Wiltshire, Paula, Wincup, Joanne, Wolf, Julia, Wong, Henna, Wong, Cyndi, Wong, Daniel, Wong, Jonathan, Wong, Shi Qin, Wood, Sarah, Wood, Henry, Wooding, Jackie, Woolley, Kelly, Wright, Myles, Wynn-Williams, Roland, Yannakou, Costas, Yeoh, Zhi Han, Yeung, David, Young, Agnes, Yuen, Flora, Yuen, Agnes, Zaja, Oliver, Zhang, Xiao-Yin, and Zhang, Mei

Published
2025
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29. Evaluation of efficacy and safety of rituximab in patients with progressive interstitial lung disease (ILD) with inflammatory component (EvER-ILD2): A multicentre double-blind placebo-controlled randomized trial

Author

Ferreira, Marion, Bejan-Angoulvant, Theodora, Marchand-Adam, Sylvain, Mousset, Elodie, Mureau, Elody, Jouneau, Stéphane, Nunes, Hilario, Montani, David, Chenivesse, Cécile, Cadranel, Jacques, Bonniaud, Philippe, Crestani, Bruno, Cottin, Vincent, and Caille, Agnès

Published
2025
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32. Actor-Critic Network for Q&A in an Adversarial Environment

Author

Sadeghian, Bejan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Significant work has been placed in the Q&A NLP space to build models that are more robust to adversarial attacks. Two key areas of focus are in generating adversarial data for the purposes of training against these situations or modifying existing architectures to build robustness within. This paper introduces an approach that joins these two ideas together to train a critic model for use in an almost reinforcement learning framework. Using the Adversarial SQuAD "Add One Sent" dataset we show that there are some promising signs for this method in protecting against Adversarial attacks., Comment: 6 pages, 3 figures, 3 tables

Published
2022

34. Catheter-based renal denervation in the treatment of arterial hypertension: An expert consensus statement on behalf of the French Society of Hypertension (SFHTA), French Society of Radiology (SFR), French Society of Interventional Cardiology (GACI), French Society of Cardiology (SFC), French Association of Private Cardiologists (CNCF), French Association of Hospital Cardiologists (CNCH), French Society of Thoracic and Cardiovascular Surgery (SFCTCV) and French Society of Vascular and Endovascular Surgery (SCVE)

Author

Pathak, Atul, Boulestreau, Romain, Sapoval, Marc, Lantelme, Pierre, Duly-Bouhanick, Beatrice, Benamer, Hakim, Bejan-Angoulvant, Theodora, Cremer, Antoine, Amar, Laurence, Delarche, Nicolas, Ormezzano, Olivier, Sabouret, Pierre, Silhol, François, Sosner, Philippe, Lopez-Sublet, Marilucy, Cohen, Ariel, Courand, Pierre Yves, and Azizi, Michel

Published
2024
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45. Rationale and design of the French cohort of acute myocarditis diagnosed by cardiac magnetic resonance imaging (MyocarditIRM)

Author

Bouleti, Claire, Servoz, Clement, Alos, Benjamin, Carsten, Ehmer, Jacquier, Alexis, Ternacle, Julien, Deux, Jean-François, Tea, Victoria, Mousseaux, Elie, Garcia, Rodrigue, Bonnet, Guillaume, Huttin, Olivier, Akodad, Mariama, Bejan-Angoulvant, Theodora, Lattuca, Benoit, Redheuil, Alban, Glatt, Nicolas, Angoulvant, Denis, and Ou, Phalla

Published
2024
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46. ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results

Author

Miolane, Nina, Caorsi, Matteo, Lupo, Umberto, Guerard, Marius, Guigui, Nicolas, Mathe, Johan, Cabanes, Yann, Reise, Wojciech, Davies, Thomas, Leitão, António, Mohapatra, Somesh, Utpala, Saiteja, Shailja, Shailja, Corso, Gabriele, Liu, Guoxi, Iuricich, Federico, Manolache, Andrei, Nistor, Mihaela, Bejan, Matei, Nicolicioiu, Armand Mihai, Luchian, Bogdan-Alexandru, Stupariu, Mihai-Sorin, Michel, Florent, Duc, Khanh Dao, Abdulrahman, Bilal, Beketov, Maxim, Maignant, Elodie, Liu, Zhiyuan, Černý, Marek, Bauw, Martin, Velasco-Forero, Santiago, Angulo, Jesus, and Long, Yanan

Subjects
Computer Science - Computational Geometry, Mathematics - Differential Geometry, Mathematics - General Topology
Abstract

This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning". The competition asked participants to provide creative contributions to the fields of computational geometry and topology through the open-source repositories Geomstats and Giotto-TDA. The challenge attracted 16 teams in its two month duration. This paper describes the design of the challenge and summarizes its main findings.

Published
2021

47. Attosecond Intramolecular-Scattering and Vibronic Delay

Author

Ghomashi, Bejan, Douguet, Nicolas, and Argenti, Luca

Subjects
Physics - Atomic Physics
Abstract

The photoionization of the CO molecule from the C$-1s$ orbital does not obey the Franck-Condon approximation, as a consequence of the nuclear recoil that accompanies the direct emission and intra-molecular scattering of the photoelectron. We use an analytical model to investigate the temporal signature of the entangled nuclear and electronic motion in this process. We show that the photoelectron emission delay can be decomposed into its localization and resonant-confinement components. Finally, photoionization by a broadband soft-x-ray pulse results in a coherent vibrational ionic state with a tunable delay with respect to the classical sudden-photoemission limit., Comment: 5 pages, 4 figures, to be presented at DAMOP 2021 in the focus session on the application of XFEL light

Published
2021
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48. Quantum computing critical exponents

Author

Dreyer, Henrik, Bejan, Mircea, and Granet, Etienne

Subjects
Quantum Physics, Condensed Matter - Statistical Mechanics
Abstract

We show that the Variational Quantum-Classical Simulation algorithm admits a finite circuit depth scaling collapse when targeting the critical point of the transverse field Ising chain. The order parameter only collapses on one side of the transition due to a slowdown of the quantum algorithm when crossing the phase transition. In order to assess performance of the quantum algorithm and compute correlations in a system of up to 752 qubits, we use techniques from integrability to derive closed-form analytical expressions for expectation values with respect to the output of the quantum circuit. We also reduce a conjecture made by Ho and Hsieh about the exact preparation of the transverse field Ising ground state to a system of equations., Comment: 16 pages, 5 figures

Published
2021
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49. Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth

Author

Abraham, Abin, Le, Brian, Kosti, Idit, Straub, Peter, Velez-Edwards, Digna R, Davis, Lea K, Newton, JM, Muglia, Louis J, Rokas, Antonis, Bejan, Cosmin A, Sirota, Marina, and Capra, John A

Subjects
Paediatrics, Reproductive Medicine, Biomedical and Clinical Sciences, Health Sciences, Pediatric, Perinatal Period - Conditions Originating in Perinatal Period, Infant Mortality, Preterm, Low Birth Weight and Health of the Newborn, Clinical Research, Prevention, Patient Safety, Conditions Affecting the Embryonic and Fetal Periods, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Reproductive health and childbirth, Good Health and Well Being, Algorithms, Electronic Health Records, Female, Gestational Age, Humans, Infant, Newborn, Machine Learning, Pregnancy, Premature Birth, Preterm birth, Machine learning, Electronic health records, Artificial intelligence, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundIdentifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.MethodsHere, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.ResultsWe find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system.ConclusionsBy leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings.

Published
2022

50. Demographic Predictors of Treatment and Complications for Spinal Disorders: Part 2, Lumbar Spine Trauma.

Author

Jammal, Omar Al, Gendreau, Julian, Alvandi, Bejan, Patel, Neal A, Brown, Nolan J, Shahrestani, Shane, Lien, Brian V, Delavar, Arash, Tran, Katelynn, Sahyouni, Ronald, Diaz-Aguilar, Luis Daniel, Gilbert, Kevin, and Pham, Martin H

Subjects
Decompression, Fusion, Lumbar spine trauma, National Inpatient Sample, Trauma, Prevention, Physical Injury - Accidents and Adverse Effects, Traumatic Head and Spine Injury, Clinical Research, Patient Safety, Evaluation of treatments and therapeutic interventions, 6.4 Surgery, Injuries and accidents, Musculoskeletal
Abstract

ObjectiveTo study the impact of demographic factors on management of traumatic injury to the lumbar spine and postoperative complication rates.MethodsData was obtained from the National Inpatient Sample (NIS) between 2010-2014. International Classification of Diseases, 9th revision, Clinical Modification codes identified patients diagnosed with lumbar fractures or dislocations due to trauma. A series of multivariate regression models determined whether demographic variables predicted rates of complication and revision surgery.ResultsA total of 38,249 patients were identified. Female patients were less likely to receive surgery and to receive a fusion when undergoing surgery, had higher complication rates, and more likely to undergo revision surgery. Medicare and Medicaid patients were less likely to receive surgical management for lumbar spine trauma and less likely to receive a fusion when operated on. Additionally, we found significant differences in surgical management and postoperative complication rates based on race, insurance type, hospital teaching status, and geography.ConclusionSubstantial differences in the surgical management of traumatic injury to the lumbar spine, including postoperative complications, among individuals of demographic factors such as age, sex, race, primary insurance, hospital teaching status, and geographic region suggest the need for further studies to understand how patient demographics influence management and complications for traumatic injury to the lumbar spine.

Published
2021

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